Data Preparation
Real data is messy. Most ML work is cleaning and preparing data — models are only as good as their inputs.
Learn it
Learn to handle missing values, remove duplicates, fix types, encode categories (numbers for text categories), and scale numeric features. Beware leaking future information into training.
By the end you’ll be able to
- Handle missing values
- Encode and scale features
Free resources
- Kaggle Learn — Data Cleaning(opens in a new tab)Start here
Hands-on missing values, dates, messy data
Free4 hr - scikit-learn — Preprocessing data (docs)(opens in a new tab)
Official docs on encoding and scaling
Free - scikit-learn — Common pitfalls & practices(opens in a new tab)
Explains data leakage and how to avoid it
Free20 min
Practice
Take a messy dataset, handle its missing values two different ways, and encode a categorical column.
Build it — your output
A reusable cleaning notebook that turns a raw CSV into a model-ready table, with each step commented.
Prove it — check yourself
You’re ready to move on when you can honestly say yes to each:
- Can you handle missing values sensibly?
- Can you encode categorical variables?
- Do you know why scaling matters for some models?
Do it with AI
Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).
The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.
- Understand itClaude / ChatGPT / Gemini
I'm a beginner learning "Data Preparation". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.
- PracticeClaude / ChatGPT / Gemini
Give me 3 short practice problems on "Data Preparation" (my goal: handle missing values), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.
- Get unstuckClaude / ChatGPT / Gemini
I'm stuck on this exercise about "Data Preparation": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.
- Build & reviewCursor / Copilot
Here is my work for a "Data Preparation" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.
- Prove itClaude / ChatGPT / Gemini
Quiz me on "Data Preparation". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.